Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity

Soybean canopy outline is an important trait used to understand light interception ability, canopy closure rates, row spacing response, which in turn affects crop growth and yield, and directly impacts weed species germination and emergence. In this manuscript, we utilize a methodology that constructs geometric measures of the soybean canopy outline from digital images of canopies, allowing visualization of the genetic diversity as well as a rigorous quantification of shape parameters. Our choice of data analysis approach is partially dictated by the need to efficiently store and analyze large datasets, especially in the context of planned high-throughput phenotyping experiments to capture time evolution of canopy outline which will produce very large datasets. Using the Elliptical Fourier Transformation (EFT) and Fourier Descriptors (EFD), canopy outlines of 446 soybean plant introduction (PI) lines from 25 different countries exhibiting a wide variety of maturity, seed weight, and stem termination were investigated in a field experiment planted as a randomized complete block design with up to four replications. Canopy outlines were extracted from digital images, and subsequently chain coded, and expanded into a shape spectrum by obtaining the Fourier coefficients/descriptors. These coefficients successfully reconstruct the canopy outline, and were used to measure traditional morphometric traits. Highest phenotypic diversity was observed for roundness, while solidity showed the lowest diversity across all countries. Some PI lines had extraordinary shape diversity in solidity. For interpretation and visualization of the complexity in shape, Principal Component Analysis (PCA) was performed on the EFD. PI lines were grouped in terms of origins, maturity index, seed weight, and stem termination index. No significant pattern or similarity was observed among the groups; although interestingly when genetic marker data was used for the PCA, patterns similar to canopy outline traits was observed for origins, and maturity indexes. These results indicate the usefulness of EFT method for reconstruction and study of canopy morphometric traits, and provides opportunities for data reduction of large images for ease in future use

Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean [Glycine max L. (Merr.)] pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot. To meet this goal, we developed a multi-view image-based yield estimation framework utilizing deep learning architectures. Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions. We used data from controlled imaging environment in field, as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation. Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort, and opening new breeding methods avenues to develop cultivars

Predictive analysis of soybean (Glycine max) phenotypes

Commercial soybean varieties in the United States can be traced back to a limited number of ancestral lines, with a clear separation of ancestors for the northern and southern states. The narrow genetic base offers opportunities in adapting genomic selection to capture the ancestral source of each allele, increasing accuracy compared to the standard marker-based methods. To validate this approach, a novel chromosome segment tracing and genomic selection method was designed and compared to traditional genomic selection approaches. The results based on data from the SoyNAM indicate an additional ~16% accuracy in genomic selection through the use of allele tracing in genomic selection.Identification of the genes responsible for observed variation in soybean phenotypes allows for rapid identification of lines containing the preferred allele for use in breeding, as well as for improving genomic prediction results through the use of a priori allele effect information. To rapidly identify candidate genes for a broad spectrum of traits, we performed a combined GWAS and meta-GWAS study of NPGS germplasm characterization trials across the United States. In addition to previously reported genes, we identified candidate genes for traits such as soybean cyst nematode resistance, amino acid composition, and pod shattering. With the identification of candidate genes for a broad group of traits, as well as new findings indicating pleiotropic effects of major genes on additional traits, we expand the understanding of genetic pathways in soybean. Soybean yield performance varies considerably across environments. In order to determine the underlying environmental cause for yield variability, we developed a machine learning approach to predict performance based on weekly weather parameters. As timing of weather events determines their effect on soybean performance, we used a method called Long Short-Term Memory which is capable of learning the relative importance of these weather parameters at different timepoints to accurately predict performance. Results from this experiment suggested that the relative importance of weather parameters differed from that commonly taught in agricultural production classes. The timing of soybean flowering and maturity varies across latitudes based on daylength and genetic influences, driving the broad adaptation of soybean varieties to latitudinal bands. While the molecular control of variation in flowering and maturity timing has been well characterized, the timing of intermediate reproductive stages has received little attention from geneticists. To better understand the influence of previously identified genes on these intermediate stages, as well as identify any previously unidentified genes controlling the timing of these intermediate stages, we conducted a concurrent GWAS study and maturity isoline study. Models generated based on previously identified genes were able to capture ~70% of the genetic variation for each of the eight soybean reproductive stages, indicating high overlap between the genetic regulation of the intermediate growth stages and that of flowering and/or maturity timing.</p

Predictive analysis of soybean (Glycine max) phenotypes

Commercial soybean varieties in the United States can be traced back to a limited number of ancestral lines, with a clear separation of ancestors for the northern and southern states. The narrow genetic base offers opportunities in adapting genomic selection to capture the ancestral source of each allele, increasing accuracy compared to the standard marker-based methods. To validate this approach, a novel chromosome segment tracing and genomic selection method was designed and compared to traditional genomic selection approaches. The results based on data from the SoyNAM indicate an additional ~16% accuracy in genomic selection through the use of allele tracing in genomic selection. Identification of the genes responsible for observed variation in soybean phenotypes allows for rapid identification of lines containing the preferred allele for use in breeding, as well as for improving genomic prediction results through the use of a priori allele effect information. To rapidly identify candidate genes for a broad spectrum of traits, we performed a combined GWAS and meta-GWAS study of NPGS germplasm characterization trials across the United States. In addition to previously reported genes, we identified candidate genes for traits such as soybean cyst nematode resistance, amino acid composition, and pod shattering. With the identification of candidate genes for a broad group of traits, as well as new findings indicating pleiotropic effects of major genes on additional traits, we expand the understanding of genetic pathways in soybean. Soybean yield performance varies considerably across environments. In order to determine the underlying environmental cause for yield variability, we developed a machine learning approach to predict performance based on weekly weather parameters. As timing of weather events determines their effect on soybean performance, we used a method called Long Short-Term Memory which is capable of learning the relative importance of these weather parameters at different timepoints to accurately predict performance. Results from this experiment suggested that the relative importance of weather parameters differed from that commonly taught in agricultural production classes. The timing of soybean flowering and maturity varies across latitudes based on daylength and genetic influences, driving the broad adaptation of soybean varieties to latitudinal bands. While the molecular control of variation in flowering and maturity timing has been well characterized, the timing of intermediate reproductive stages has received little attention from geneticists. To better understand the influence of previously identified genes on these intermediate stages, as well as identify any previously unidentified genes controlling the timing of these intermediate stages, we conducted a concurrent GWAS study and maturity isoline study. Models generated based on previously identified genes were able to capture ~70% of the genetic variation for each of the eight soybean reproductive stages, indicating high overlap between the genetic regulation of the intermediate growth stages and that of flowering and/or maturity timing

Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning

Accurate prediction of crop yield supported by scientific and domain-relevant insights, can help improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production including erratic rainfall and temperature variations. We used historical performance records from Uniform Soybean Tests (UST) in North America spanning 13 years of data to build a Long Short Term Memory - Recurrent Neural Network based model to dissect and predict genotype response in multiple-environments by leveraging pedigree relatedness measures along with weekly weather parameters. Additionally, for providing explainability of the important time-windows in the growing season, we developed a model based on temporal attention mechanism. The combination of these two models outperformed random forest (RF), LASSO regression and the data-driven USDA model for yield prediction. We deployed this deep learning framework as a 'hypotheses generation tool' to unravel GxExM relationships. Attention-based time series models provide a significant advancement in interpretability of yield prediction models. The insights provided by explainable models are applicable in understanding how plant breeding programs can adapt their approaches for global climate change, for example identification of superior varieties for commercial release, intelligent sampling of testing environments in variety development, and integrating weather parameters for a targeted breeding approach. Using DL models as hypothesis generation tools will enable development of varieties with plasticity response in variable climatic conditions. We envision broad applicability of this approach (via conducting sensitivity analysis and "what-if" scenarios) for soybean and other crop species under different climatic conditions.This is a pre-print of the article Shook, Johnathon, Tryambak Gangopadhyay, Linjiang Wu, Baskar Ganapathysubramanian, Soumik Sarkar, and Asheesh K. Singh. "Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning." arXiv preprint arXiv:2006.13847 (2020). Posted with permission.</p

Integrating genotype and weather variables for soybean yield prediction using deep learning

Realized performance of complex traits is dependent on both genetic and environmental factors, which can be difficult to dissect due to the requirement for multiple replications of many genotypes in diverse environmental conditions. To mediate these problems, we present a machine learning framework in soybean (Glycine max (L.) Merr.) to analyze historical performance records from Uniform Soybean Tests (UST) in North America, with an aim to dissect and predict genotype response in multiple envrionments leveraging pedigree and genomic relatedness measures along with weekly weather parameters. The ML framework of Long Short Term Memory - Recurrent Neural Networks works by isolating key weather events and genetic interactions which affect yield, seed oil, seed protein and maturity enabling prediction of genotypic responses in unseen environments. This approach presents an exciting avenue for genotype x environment studies and enables prediction based systems. Our approaches can be applied in plant breeding programs with multi-environment and multi-genotype data, to identify superior genotypes through selection for commercial release as well as for determining ideal locations for efficient performance testing.This is a pre-print made available through bioRxiv, doi: 10.1101/331561.</p

Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity

Soybean canopy outline is an important trait used to understand light interception ability, canopy closure rates, row spacing response, which in turn affects crop growth and yield, and directly impacts weed species germination and emergence. In this manuscript, we utilize a methodology that constructs geometric measures of the soybean canopy outline from digital images of canopies, allowing visualization of the genetic diversity as well as a rigorous quantification of shape parameters. Our choice of data analysis approach is partially dictated by the need to efficiently store and analyze large datasets, especially in the context of planned high-throughput phenotyping experiments to capture time evolution of canopy outline which will produce very large datasets. Using the Elliptical Fourier Transformation (EFT) and Fourier Descriptors (EFD), canopy outlines of 446 soybean plant introduction (PI) lines from 25 different countries exhibiting a wide variety of maturity, seed weight, and stem termination were investigated in a field experiment planted as a randomized complete block design with up to four replications. Canopy outlines were extracted from digital images, and subsequently chain coded, and expanded into a shape spectrum by obtaining the Fourier coefficients/descriptors. These coefficients successfully reconstruct the canopy outline, and were used to measure traditional morphometric traits. Highest phenotypic diversity was observed for roundness, while solidity showed the lowest diversity across all countries. Some PI lines had extraordinary shape diversity in solidity. For interpretation and visualization of the complexity in shape, Principal Component Analysis (PCA) was performed on the EFD. PI lines were grouped in terms of origins, maturity index, seed weight, and stem termination index. No significant pattern or similarity was observed among the groups; although interestingly when genetic marker data was used for the PCA, patterns similar to canopy outline traits was observed for origins, and maturity indexes. These results indicate the usefulness of EFT method for reconstruction and study of canopy morphometric traits, and provides opportunities for data reduction of large images for ease in future use.This article is published as Jubery, Talukder Z., Johnathon Shook, Kyle Parmley, Jiaoping Zhang, Hsiang S. Naik, Race Higgins, Soumik Sarkar, Arti Singh, Asheesh K. Singh, and Baskar Ganapathysubramanian. "Deploying Fourier coefficients to unravel soybean canopy diversity." Frontiers in Plant Science 7 (2016). DOI:10.3389/fpls.2016.02066. Posted with permission.</p

Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean (Glycine max L. (Merr.)) pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot. To meet this goal, we developed a multiview image-based yield estimation framework utilizing deep learning architectures. Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions. We used data from controlled imaging environment in field, as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation. Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort and opening new breeding method avenues to develop cultivars.This article is published as Riera, Luis G., Matthew E. Carroll, Zhisheng Zhang, Johnathon M. Shook, Sambuddha Ghosal, Tianshuang Gao, Arti Singh, Sourabh Bhattacharya, Baskar Ganapathysubramanian, Asheesh K. Singh, and Soumik Sarkar. "Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications." Plant Phenomics 2021 (2021). DOI: 10.34133/2021/9846470. Posted with permission.</p

Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean [Glycine max L. (Merr.)] pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot. To meet this goal, we developed a multi-view image-based yield estimation framework utilizing deep learning architectures. Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions. We used data from controlled imaging environment in field, as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation. Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort, and opening new breeding methods avenues to develop cultivars.This is a pre-print of the article Riera, Luis G., Matthew E. Carroll, Zhisheng Zhang, Johnathon M. Shook, Sambuddha Ghosal, Tianshuang Gao, Arti Singh et al. "Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications." arXiv preprint arXiv:2011.07118 (2020).</p